Prediction of tool wear using regression and ANN models in end-milling operation

被引:164
作者
Palanisamy, P. [2 ]
Rajendran, I. [1 ]
Shanmugasundaram, S. [3 ]
机构
[1] Bannari Amman Inst Technol, Dept Mech Engn, Sathyamangalam 638401, Tamil Nadu, India
[2] Kumaraguru Coll Technol, Dept Mech Engn, Coimbatore 641006, Tamil Nadu, India
[3] Govt Coll Technol, Coimbatore 641013, Tamil Nadu, India
关键词
tool wear; Design of Experiments (DOE); regression model; artificial neural network (ANN);
D O I
10.1007/s00170-007-0948-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 [计算机科学与技术];
摘要
Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.
引用
收藏
页码:29 / 41
页数:13
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